1. Field of the Invention
This invention generally relates to the field of knowledge management, and more specifically to a system and method for controlling skill acquisition, e.g. the transfer of skills from an expert to a novice, using gaze scanning behavior through a user interface.
2. Background Art
The gap in organizational knowledge that is created when an expert retires or leaves an organization is a problem that has been studied in the field of knowledge management for many years. Prior solutions have required the expert to specify everything he does in a given area or for a given task. The resulting document is then stored for others to read. One of the primary obstacles to the transfer of knowledge from an expert to a novice is that tacit knowledge is not something that experts are able to specify since they are often not even aware of how or why they do things a certain way. Secondly experts think in more abstract concepts than novices, thus their explanations can be hard for novices to assimilate. Lastly, knowledge is not easily acquired by reading a document.
Most gaze tracking devices, such as SensoMotoric® or ™ manufactured by SensoMotoric Instruments and faceLAB® manufactured by Seeing Machines, operate based upon the principal that the direction of a person's gaze is directly related to the relative positions of the pupil and the reflection of an object off the cornea (gaze tracking is often termed “eye tracking”). These devices often include image processing capabilities that operate on a video image of an eye to determine the gaze direction of the eye. These image processing capabilities are enhanced by using the bright eye affect.
The bright eye affect is a result of the highly reflective nature of the retina. This characteristic of the retina means that a significant amount of the light that enters an eye is reflected back through the pupil. Thus, when light shines into an eye along the axis of a camera lens, the retina reflects a significant portion of the light back to the camera. Hence, the pupil appears as a bright disk to the camera. This affect allows the pupil to be more readily imaged from a video of an eye.
Other systems and methods exist for gaze tracking. Some systems implement two video cameras, one for tracking head movement and the other for measuring a reflection off of the eyes. Other mechanisms involve measuring electric potential differences between locations on different sides of an eye. High accuracy devices are very intrusive on the user and require that the user's head be held in a fixed position or that the user wear special equipment to track the eye.
Recently, an eye gaze eye tracking system has been developed as described in The Eyegaze Eyetracking System—Unique Example of a Multiple-Use Technology, 4th Annual 1994 IEEE Dual-Use Technologies and Applications Conference, May, 1994. This system comprises a video camera located below a computer display that monitors one of the user's eyes. The device also contains an infrared light emitting diode (LED) located at the center of the camera's lens to maximize the bright-eye affect. Image processing software on the computer computes the user's gaze point on the display sixty times a second with an accuracy of about a quarter inch.
Gaze tracking devices have been used for weapon control, operator training, usability analysis, market research, and as an enablement for the disabled. However, gaze patterns and reacting to gaze in a user interface have not been applied together in an adaptive user interface for the purpose of skill acquisition.
The idea of analyzing gaze patterns for differences between novices and experts is not novel. Kasarskis et al. 2001 showed differences in performance and eye movements between expert and novice pilots who performed landings in a flight simulator. Additionally, other studies have shown that differences exist between expert and novice gaze patterns in the following areas: radiology, basketball, airplane piloting.
There is prior art in using gaze in accessibility systems for “selection” when the user has physical motor impairments.
However, these two concepts of differences in gaze patterns and reacting to gaze in a user interface have not been applied together in an adaptive user interface for the purpose of skill acquisition. It would be highly desirable to provide a novel system and method for controlling skill acquisition interfaces that captures what an expert does through recognition technologies (e.g. eye tracking), storing the implicit data, and then adjusting the interface for a novice based on deviations from this standard.